IL282011B1 - Tree-based data exploration and data-driven protocol - Google Patents

Tree-based data exploration and data-driven protocol

Info

Publication number
IL282011B1
IL282011B1 IL282011A IL28201121A IL282011B1 IL 282011 B1 IL282011 B1 IL 282011B1 IL 282011 A IL282011 A IL 282011A IL 28201121 A IL28201121 A IL 28201121A IL 282011 B1 IL282011 B1 IL 282011B1
Authority
IL
Israel
Prior art keywords
patient
vad
survival rate
pump
impella
Prior art date
Application number
IL282011A
Other languages
Hebrew (he)
Other versions
IL282011B2 (en
IL282011A (en
Original Assignee
Abiomed Inc
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Filing date
Publication date
Application filed by Abiomed Inc filed Critical Abiomed Inc
Publication of IL282011A publication Critical patent/IL282011A/en
Publication of IL282011B1 publication Critical patent/IL282011B1/en
Publication of IL282011B2 publication Critical patent/IL282011B2/en

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    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M60/00—Blood pumps; Devices for mechanical circulatory actuation; Balloon pumps for circulatory assistance
    • A61M60/50—Details relating to control
    • A—HUMAN NECESSITIES
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    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
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    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
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    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
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    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
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    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
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    • A61M60/109—Extracorporeal pumps, i.e. the blood being pumped outside the patient's body incorporated within extracorporeal blood circuits or systems
    • A61M60/113—Extracorporeal pumps, i.e. the blood being pumped outside the patient's body incorporated within extracorporeal blood circuits or systems in other functional devices, e.g. dialysers or heart-lung machines
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    • A61M60/122—Implantable pumps or pumping devices, i.e. the blood being pumped inside the patient's body
    • A61M60/165—Implantable pumps or pumping devices, i.e. the blood being pumped inside the patient's body implantable in, on, or around the heart
    • A61M60/17—Implantable pumps or pumping devices, i.e. the blood being pumped inside the patient's body implantable in, on, or around the heart inside a ventricle, e.g. intraventricular balloon pumps
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    • A61M60/178—Implantable pumps or pumping devices, i.e. the blood being pumped inside the patient's body implantable in, on, or around the heart drawing blood from a ventricle and returning the blood to the arterial system via a cannula external to the ventricle, e.g. left or right ventricular assist devices
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    • A—HUMAN NECESSITIES
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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61M60/00—Blood pumps; Devices for mechanical circulatory actuation; Balloon pumps for circulatory assistance
    • A61M60/50—Details relating to control
    • A61M60/592—Communication of patient or blood pump data to distant operators for treatment purposes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2205/00—General characteristics of the apparatus
    • A61M2205/04—General characteristics of the apparatus implanted
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61M2205/00—General characteristics of the apparatus
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    • A61M2205/3331—Pressure; Flow
    • A—HUMAN NECESSITIES
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    • A61M2205/50—General characteristics of the apparatus with microprocessors or computers
    • A—HUMAN NECESSITIES
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    • A61M2210/00—Anatomical parts of the body
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    • A61M2230/00—Measuring parameters of the user
    • A61M2230/30—Blood pressure

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  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Public Health (AREA)
  • Cardiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
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  • Computer Vision & Pattern Recognition (AREA)
  • Physiology (AREA)
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  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Fuzzy Systems (AREA)
  • Pulmonology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Urology & Nephrology (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Vascular Medicine (AREA)
  • Databases & Information Systems (AREA)

Description

0279373266- ד"פשת ,באב 'ג ,טסוגואב 20 דובכל םיטנטפה םשר 'סמ לעופה טרופס תדוגא בוחר 'סמ ןיינב ,יגולונכטה ןגה םילשורי 96951 ןמרקא טרבלא רמ :ידיל ,.נ.ג.א :ןודנה 'סמ לארשיב טנטפ תשקב 282011 החנומ לוקוטורפו םיצע ססובמ םינותנ רקח" "םינותנ םשב Abiomed, Inc . :ןיכומיס .ק.פ 26 םוימ 10/04/2024 :ונרפסמ 2793732 תילארשיה השקבה לוביק תא תיתשהל תניינועמ תשקבמה ,ןיכומיסבש הניחבה חודל הנעמב ורפסמש ליבקמ יאקירמא טנטפ ךמס לע ,ל"נה US 11,955,214 ףיעסל םאתהב 17 קוחל )ג( .םיטנטפה :ליבקמה יאקירמאה טנטפל םימיאתמה ,םיאבה םיפדה תאזב םישגומ ,ךכיפל םידומע הסרג רפסמ יפד תועיבת 25-31 :םיאבה םיפדה לש תינורטקלא הסרג תפרוצמ ,םשרה תוארוהל םאתהב . ;םהילע םינמוסמ םינוקיתה םע םיפדה . ;םינוקיתה ןומיס אלל םיפד . .ליבקמה יאקירמאה טנטפה קתעה .ליבקמה יאקירמאה טנטפה תרגסמב ןחבנ הניחבה חודב ןחובה ידי לע טטוצ רשא םוסרפה ה טנטפ ה יאקירמא 11,955,214 םדוק המסרופ אל החפשמב השקב ףא לבא ,ךשמה תשקב אוה .תילארשיה השקבה לש הרוכבה ךיראתל ףיעסל םאתהב תשקבמה לש תכשמתמה התבוחל םאתהב 18 בל תמושת תא םינפמ ונא ,קוחל .ליבקמה יאקירמאה טנטפה רעש ףד יבג לע םיטוטיצל ןחובה .השקבה לוביק לע תעכ הדונ ,ןכ לע .תוגשהה לכ לע תרבגתמ ליעל ונתשקב יכ םירובס ונא – 2 –

Claims (35)

V 0279373268- CLAIMS:
1. A system for providing a treatment recommendation to a physician for treating a patient, the system comprising one or more processors configured to: determine a first treatment recommendation based on a combination of selected patient demographics from a patient data repository applicable to the patient and operational parameters of a plurality of ventricular assist devices (VADs) suitable for treating the patient, wherein the first treatment recommendation has a first survival rate and comprises use of a first VAD, and wherein the patient data repository comprises data from treatment of acute myocardial infarction (AMI) patients, high-risk percutaneous coronary interventions (PCI) patients, or patients in cardiogenic shock; obtain, from the first VAD, a first signal; determine a second treatment recommendation based on the first signal and the first treatment recommendation, wherein the second treatment recommendation has a second survival rate; and provide the second treatment recommendation to the physician if the second survival rate is higher than the first survival rate.
2. The system of claim 1, wherein the one or more processors are further configured to: inform the physician to continue using the first VAD to treat the patient if the second survival rate is not higher than the first survival rate.
3. The system of claim 1, wherein each VAD comprises at least one sensor for providing the first signal.
4. The system of claim 1, wherein the first signal comprises information concerning a patient's vitals.
5. The system of claim 1, wherein the first signal comprises at least one of: V 0279373268- Mean Arterial Pressure (MAP), Left Ventricular Pressure (LVP), Left Ventricular End-Diastolic Pressure (LVEDP), Pulmonary Arterial Wedge Pressure (PAWP), Pulmonary Capillary Wedge Pressure (PCWP), or Pulmonary Artery Occlusion Pressure (PAOP).
6. The system of claim 1, wherein the one or more processors are further configured to: control operation of the first VAD or a second VAD to provide the patient with the second treatment recommendation.
7. The system of claim 1, wherein the first VAD comprises at least one of: an Impella® pump, an Extracorporeal Membrane Oxygenation (ECMO) pump, a balloon pump, or a Swan-Ganz catheter.
8. The system of claim 7, wherein the Impella® pump comprises any one of: Impella 2.5® pump, an Impella 5.0® pump, an Impella CP® pump, an Impella RP® pump and an Impella LD® pump.
9. The system of claim 1, wherein the second treatment recommendation comprises continuing use of the first VAD from the first treatment recommendation in addition to a second VAD.
10. The system of claim 1, wherein the second treatment recommendation comprises the use of at least one of the following for treating the patient: the first VAD, a second VAD, or no VAD.
11. The system of claim 1, wherein the patient is in cardiogenic shock.
12. The system of claim 1, wherein the first treatment recommendation is determined with a prediction model. V 0279373268-
13. The system of claim 12 , wherein the prediction model is based on a machine learning algorithm comprising any one of: a bagging and random forest algorithm, a logistic regression algorithm, a classification decision tree algorithm, a deep learning algorithm, a naïve Bayes algorithm, or a support vector machines algorithm.
14. The system of claim 1, wherein the selected patient demographics comprise: gender, age, region, duration of support, indication for use, and insertion site.
15. The system of claim 1, wherein the one or more processors are further configured to: provide the survival rate for each available VAD to a display for display; and identify the VAD with the highest survival rate.
16. The system of claim 1, wherein the one or more processors are further configured to: provide the combination of the selected patient demographics used for determining the survival rates to a display for display using a branched-tree representation.
17. The system of claim 1, wherein each survival rate comprises a probability of survival of a patient belonging to the combination of the selected patient demographics when treated with a VAD.
18. The system of claim 1, wherein the first signal is obtained after the first VAD has been used to treat the patient.
19. The system of claim 1further comprising the first VAD. V 0279373268-
20. A system for providing a treatment recommendation to a physician for treating a patient, the system comprising one or more processors configured to: obtain selected patient demographics applicable to the patient from a patient data repository, wherein the patient data repository comprises data from treatment of acute myocardial infarction (AMI) patients, high-risk percutaneous coronary interventions (PCI) patients, or patients in cardiogenic shock; determine at least one ventricular assist device (VAD) suitable for treating the patient; determine, using a prediction model, a survival rate for each suitable VAD based on data from the patient data repository for a combination of the selected patient demographics applicable to the patient; and provide a recommended first VAD associated with the highest survival rate to the physician.
21. The system of claim 20 , wherein the one or more processors are further configured to: provide the physician with the survival rate for each suitable VAD for all combinations of the selected patient demographics applicable to the patient.
22. The system of claim 20 , wherein the one or more processors are further configured to: provide the physician with a survival rate for not using a VAD for each combination of the selected patient demographics applicable to the patient.
23. The system of claim 20 , wherein the first VAD comprises at least one of: an Impella 2.5® pump, an Impella 5.0® pump, an Impella CP® pump, an Impella RP® pump, or an Impella LD® pump.
24. The system of claim 23 , wherein the Impella pump comprises any one of: V 0279373268- an Impella 2.5® pump, an Impella 5.0® pump, an Impella CP® pump, an Impella RP® pump, or an Impella LD® pump.
25. The system of claim 20 , wherein the patient demographics comprise: age, gender, region, year of implantation, support device, duration of support, insertion site, and ejection fraction.
26. The system of claim 20 , wherein the prediction model uses a machine learning algorithm to determine each survival rate.
27. The system of claim 26 , wherein the machine learning algorithm comprises any one of: a bagging and random forest algorithm, a logistic regression algorithm, a classification decision tree algorithm, a deep learning algorithm, a naïve Bayes algorithm, or a support vector machines algorithm.
28. The system of claim 20 , wherein the combination of the selected patient demographics follows a tree-model.
29. The system of claim 28 , wherein the tree-model has an order of any one of: two, three, four, five and six.
30. The system of claim 20 , wherein the one or more processors are further configured to: control operation of the recommended first VAD.
31. The system of claim 20 , wherein the one or more processors are further configured to: provide the survival rate for each available VAD to a display for display.
32. The system of claim 20 , wherein the one or more processors are further configured to: V 0279373268- provide the combination of the selected patient demographics used for determining the survival rates to a display for display using a branched-tree representation.
33. The system of claim 20 , wherein each survival rate comprises a probability of survival of a patient belonging to the combination of the selected patient demographics when treated with a VAD.
34. A non-transitory computer readable storage medium having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to: determine a first treatment recommendation based on a combination of selected patient demographics from a patient data repository applicable to a patient and operational parameters of a plurality of ventricular assist devices (VADs) suitable for treating the patient, wherein the first treatment recommendation has a first survival rate and comprises use of a first VAD, and wherein the patient data repository comprises data from treatment of acute myocardial infarction (AMI) patients, high-risk percutaneous coronary interventions (PCI) patients, or patients in cardiogenic shock; obtain, from the first VAD, a first signal; determine a second treatment recommendation based on the first signal and the first treatment recommendation, wherein the second treatment recommendation has a second survival rate; and provide the second treatment recommendation to a physician if the second survival rate is higher than the first survival rate.
35. A non-transitory computer readable storage medium having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to: V 0279373268- obtain selected patient demographics applicable to a patient from a patient data repository, wherein the patient data repository comprises data from treatment of acute myocardial infarction (AMI) patients, high-risk percutaneous coronary interventions (PCI) patients, or patients in cardiogenic shock; determine at least one ventricular assist device (VAD) suitable for treating the patient; determine, using a prediction model, a survival rate for each suitable VAD based on data from the patient data repository for a combination of the selected patient demographics applicable to the patient; and provide a recommended first VAD associated with the highest survival rate to a physician.
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